The number of patients suffering from motor neuropathies increases every year. Traumatic spinal cord injury, stroke, neuro-degenerative diseases, and amputations are a few of the conditions that lead to motor control deficits. Patients with such conditions can benefit from prosthetic technologies to replace missing or nonfunctional limbs, or technologies to restore muscle control and function.
Traditionally, Brain Machine Interface (BMI) research has attempted to find functional relationships between neuronal activity and goal directed movements using supervised learning (SL) techniques in an input-output modeling framework. This approach fundamentally requires knowledge of the inputs (neural activity) and a desired response (behavioral kinematics). However, the requirement of behavioral kinematics (patient movements) is a fundamental limitation when applying BMI technology in the clinical setting with patients that may be otherwise unable to provide a desired response (e.g. physical movements) to train the BMI system.
Accordingly a need exists for a system and method for BMI control that uses a semi-supervised learning paradigm, such as reinforcement learning. This control system allows the patient to learn to control the BMI. Additionally, the BMI can adapt to the patient's behavior.